Language models (LMs) are increasingly used to generate decisions and explanations for high-stakes financial tasks, but these explanations can contain factual inconsistencies with the underlying claim or invalid causal reasoning. We propose a two-step auditing framework that decomposes each explanation into reasoning points and verifies them using natural language inference (NLI): (i) claim ⇒ point for factuality verification and (ii) point ⇒ conclusion for causality verification. To reduce reliance on expensive expert annotations, we study effective fine-tuning strategies based on semi-supervised learning (SSL) for NLI verifiers, leveraging abundant unlabeled explanations via pseudo-labeling and FreeMatch with self-adaptive confidence thresholds. Experiments across small language models and both encoder-only and decoder-only verifiers under varying labeling budgets show that SSL consistently outperforms the pre-trained baseline and supervised fine-tuning. FreeMatch is generally more robust than simple pseudo-labeling, suggesting better utilization of unlabeled data for scaling error detection of LMs’ generations.